Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.

Authors

Luca Schlegel, Fabio Gómez Cano, Alexandre P Marand, Frank Johannes

Published in

NAR genomics and bioinformatics. Volume 8. Issue 2. Pages lqag063. Epub Jun 24, 2026.

Abstract

Scalable proxies for 3D genome contacts-such as single-cell co-accessibility and deep learning predictions-have emerged as powerful alternatives to chromatin capture-based methods, but predictions systematically overestimate long-range interactions. Here we show how to correct this bias using distance-based penalty functions informed by Gaussian mixture modeling and polymer-physics scaling. Using Hi-C datasets from maize, rice, and soybean, we derive tissue-specific and global consensus penalties parameterized by multiregime power-law exponents. Applying these corrections to single-cell ATAC sequencing co-accessibility scores improves their distance profiles in concordance with Hi-C and reduces long-range false positives by an average of 73% with tissue-specific penalties and 66% with the global consensus. We provide open-source code and fitted parameters to support adoption in maize, rice, and soybean.

PMID:
42359001
Bibliographic data and abstract were imported from PubMed on 26 Jun 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.